International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Comparatives Evaluation of Machine Learning Techniques for Twitter Cyberbullying Detection |
|---|---|
| ABSTRACT | Cyberbullying is now a common problem on social media platforms such as Twitter, leading to serious consequences for victims. Detecting and preventing cyber bullying is crucial for maintaining a safe online environment. These days, cyberbullying is a prevalent issue on social media networks such, including traditional classifiers like Convolutional Neural Networks (CNNs), Support Vector Machines and Decision Trees, and other deep learning methods LSTM, or long short-term memory networks. We experiment with different feature representations, such as bag-of-words, word embedding, and character-level representations, to convey the subtleties of cyber bullying language. Additionally, we investigate the effects of data imbalance and investigate methods like oversampling and under sampling to mitigate it. We use labeled datasets of cyberbullying tweets to assess each model's performance Regarding F1-score, recall, accuracy, and precision. The comparison's outcomes analysis provide insight into the benefits and drawbacks of several machines learning techniques for Twitter cyberbullying detection, aiding in the creation of more potent detection tools and tactics. |
| AUTHOR | NITHIN S, MAHESHWARI M DESAI PG Student, Dept. of MCA, City Engineering College, Bengaluru, India Assistant Professor, Dept. of MCA, City Engineering College, Bengaluru, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312060 |
| pdf/60_A Comparatives Evaluation of Machine Learning Techniques for Twitter Cyberbullying Detection.pdf | |
| KEYWORDS | |
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